Posts

Ever handed your AI a plate at an all-you-can-eat buffet and whispered, “Go wild”? Bad idea. Here’s what happened next and why it’s the perfect metaphor for unbounded input consumption in LLMs. 🧠🍕 Unbounded Consumption: The Buffet That Never Ends You know when you walk into a buffet and tell yourself, “Just a small plate, nothing wild”? Cut to: you’re on your third round of sushi, mac & cheese, and something suspiciously on fire. Now your stomach is in full blown “what have you done” mode. ...

🧠 What’s the Deal with Misinformation in LLMs? Okay, let’s break this down: Imagine you’ve got a friend who’s known for spinning wild tales. Sometimes, they tell a story that’s just too good to be true. That’s exactly what’s happening when LLMs generate hallucinations—plausible-sounding, but totally false information. They don’t know what’s true; they’re just filling in gaps from their training data. But it doesn’t stop there. Overreliance on these AI outputs without verification is like taking every story your friend tells as gospel big mistake, right? That’s what we need to avoid in the world of LLMs. 😱 ...

🧠 What the Heck Are Vectors and Embeddings? Okay, let’s break it down: Imagine you’re throwing a party, and you invite a bunch of people (external knowledge) to join the fun. You give them name tags (vectors) so you can remember who they are and what they do (embeddings). Now, let’s say, while you’re busy mingling, someone sneaks in with a fake name tag and starts giving out your Wi-Fi password to everyone. That’s essentially what happens when vectors and embeddings aren’t managed properly. 😱 ...

You ever tell a toddler a secret and then regret it two minutes later because they’ve told everyone at the dinner table? Yeah. That’s your LLM system prompt if you’re not careful. Welcome to LLM07: System Prompt Leakage, the most underrated “oops” in GenAI security. 🧠 What’s a System Prompt? Think of it as the behind the scenes script you whisper to your AI: “Hey buddy, always be polite, never mention user passwords, and definitely don’t say ‘I’m connected to the database using root access.’” ...

Excessive Agency in LLMs: When Giving Too Much Power Goes Wrong Description: An LLM-based system is often granted a degree of agency by its developer the ability to call functions or interface with other systems via extensions (sometimes referred to as tools, skills, or plugins by different vendors) to undertake actions in response to a prompt. The decision over which extension to invoke may also be delegated to an LLM ‘agent’ to dynamically determine based on input prompt or LLM output. Agent based systems will typically make repeated calls to an LLM using output from previous invocations to ground and direct subsequent invocations. ...

🎭 The Plot Twist You Didn’t See Coming Improper Output Handling is like giving a parrot the microphone at a live news broadcast. Whatever it hears, it repeats unfiltered, unedited, and potentially career-ending. 🦜🎙️ LLMs are incredible at processing and generating content, but without proper output handling, they can accidentally introduce XSS, SQL injection, or even remote code execution (RCE) into your system. Essentially, you’re playing cybersecurity roulette. 🎰🔫 🚨 Why This is a Disaster Waiting to Happen Picture this: You tell an AI to summarize an article, and instead of just summarizing, it sneaks in a JavaScript payload. Or you use it to generate SQL queries, and it casually suggests dropping your entire database. 💀 ...

☠️ Data Poisoning in AI: How Your Model Might Be a Sleeper Agent! Welcome back to my AI Security & Red Teaming series! 🎭 Today’s villain? 👉 Data Poisoning where attackers inject toxic, misleading, or malicious data into your AI model, making it say or do things it wasn’t meant to. Imagine you’re training a guard dog to protect your house. But someone slips in false training now your dog thinks burglars are its best friends while attacking the mailman instead. 📦🐶💀 ...

🔗 LLM Supply Chain: Who’s Messing with My AI Ingredients? (OWASP LLM Top 10) Welcome back, cyber enthusiasts! 🔥 In today’s AI LLM Red Teaming series, we’re diving into another juicy vulnerability from the OWASP Top 10 for LLMs: the Supply Chain. If you think supply chains are just for shipping products, think again! For LLMs, the supply chain covers everything training data, pre-trained models, fine-tuning adapters, and even deployment platforms. And, oh boy, the risks are everywhere. 🫠 ...

What Is Sensitive Information Disclosure? Imagine you’re at a dinner party, and someone starts sharing private stories they overheard about you from another guest. Not cool, right? 😬 This is what happens when Large Language Models (LLMs) spill secrets they shouldn’t. Sensitive information includes PII (personal identifiable information) like your name or address, business secrets, or even proprietary algorithms. When LLMs, like the friendly AI waiter, “accidentally” reveal these secrets, it can lead to privacy violations and intellectual property theft. ...

🤖 Hacking AI: The Art of Prompt Injection (OWASP LLM Top 10) Hey there, fellow cyber adventurers! 🔥 Welcome to my new series on AI LLM Red Teaming, where I walk you through the OWASP Top 10 LLM vulnerabilities like a hacker in a candy store. 🍭 Today’s topic? Prompt Injection the cybersecurity equivalent of convincing your friend to say something stupid on live TV. 🎤😆 What is Prompt Injection? Imagine you’ve got a super-smart AI model that follows instructions like an obedient intern. Now, what if you could trick it into revealing secrets, breaking rules, or even executing unintended actions? 🤯 ...